Drugs and the Statistics group

Congratulations
are due to two members of the Statistics research group who had
adjacent papers in a recent issue of the Journal of the Royal
Statistical Society, Series C, Applied Statistics.

JRSS
C is a journal with long-standing reputation in attracting
high-quality papers from a wide range of application areas, in which
specific problems lead to interesting statistical challenges and
creative solutions to these challenges.

The
first was by Colin
Aitken and Amy
Wilson (now at the University of Durham) and co-authors from Mass
Spec Analytical in Filton near Bristol:

The
evaluation of evidence for auto-correlated data in relation to traces
of cocaine on banknotes.

“There
has been much work in recent years in the development of statistical
models for the evaluation of forensic scientific evidence for
multivariate hierarchical random effects continuous models with
independent data. The methods described in this article extend the
univariate model to autocorrelated data. Application of the methods
is illustrated with data concerning the quantities of cocaine on
seizures of banknotes. Calculations are made for the strength of the
support provided by the evidence for the proposition that the
banknotes are associated with a person who is associated with a
criminal activity involving cocaine in contrast to the proposition
that the banknotes are associated with a person who is not associated
with a criminal activity involving cocaine.”

The
second was by Ioannis
Papastathopoulos
with a co-author from Lancaster University:

“The clinical trial
statistician concerned with efficacy is concerned with characterising
the expected response patients have to an experimental drug. When it
comes to safety, what is important is the characterization of the
unexpected responses, i.e., the extreme values. For safety data,
unlike efficacy, the questions are typically not well-defined, some
of them will not be known until the data has been studied in some
detail, and the data are usually messy. For these reasons,
statisticians have tended to shy away from the analysis and
particularly modelling of safety data, leaving interpretation to
clinicians. In this article, new statistical methods that identify
signals of drug toxicity are developed and applied to safety data
from a phase 3 clinical trial of a drug that has been linked to
toxicity. The methods allow for the testing
of the hypothesis of ordered dependence between doses in laboratory
variables and for
estimating
the probability of
joint extreme elevations. This work was supported by AstraZeneca and
is used to aid their
decision making in accepting/rejecting experimental drugs“

This article was published on Mar 3, 2016

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